def test_category_interaction():
c = pd.Series(pd.Categorical([0, 0, 0, 1, 1, 1]))
actual = category_interaction(c, precondition=False).A
expected = np.zeros((6, 2))
expected[:3, 0] = 1.0
expected[3:, 1] = 1.0
assert_allclose(actual, expected)
actual = category_interaction(c, precondition=True).A
cond = np.sqrt((expected**2).sum(0))
expected /= cond
assert_allclose(actual, expected)
def test_interaction_cat_only(cat):
interact = Interaction(cat=cat)
assert interact.nobs == cat.shape[0]
assert_frame_equal(cat, interact.cat)
expected = category_interaction(category_product(cat), precondition=False)
actual = interact.sparse
assert isinstance(actual, csc_matrix)
assert_allclose(expected.A, actual.A)
def test_interaction_cat_cont(cat, cont):
interact = Interaction(cat=cat, cont=cont)
assert interact.nobs == cat.shape[0]
assert_frame_equal(cat, interact.cat)
assert_frame_equal(cont, interact.cont)
base = category_interaction(category_product(cat), precondition=False).A
expected = []
for i in range(cont.shape[1]):
element = base.copy()
element[np.where(element)] = cont.iloc[:, i].to_numpy()
expected.append(element)
expected = np.column_stack(expected)
actual = interact.sparse
assert isinstance(actual, csc_matrix)
assert_allclose(expected, interact.sparse.A)